Amina Bolatkan
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
-
- RNA modifications and cancer 3
- Cancer-related gene regulation 3
-
- Cancer Genomics and Diagnostics 3
- Co-authors
- Ryuji Hamamoto (12 shared papers)Masaaki Komatsu (10 shared papers)Ken Asada (10 shared papers)Syuzo Kaneko (10 shared papers)Kazuma Kobayashi (9 shared papers)Ken Takasawa (8 shared papers)Satoshi Takahashi (7 shared papers)Norio Shinkai (7 shared papers)
- Journals
- Biomolecules (4 papers)Biomedicines (2 papers)Cancers (1 paper)Journal of Personalized Medicine (1 paper)Journal of Medical Systems (1 paper)
- Partner nations
- JapanUnited KingdomUnited States
In The Last Decade
Amina Bolatkan
12 papers receiving 404 citations
Amina Bolatkan's Hit Papers
Peers
Comparison fields: 5 of 101
- Health Informatics 61
- Radiology, Nuclear Medicine and Imaging 147
- Cancer Research 78
- Artificial Intelligence 114
- Health Information Management 15
Countries citing papers authored by Amina Bolatkan
This map shows the geographic impact of Amina Bolatkan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Amina Bolatkan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amina Bolatkan more than expected).
Fields of papers citing papers by Amina Bolatkan
This network shows the impact of papers produced by Amina Bolatkan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Amina Bolatkan. The network helps show where Amina Bolatkan may publish in the future.
Co-authors
The 25 scholars most cited alongside Amina Bolatkan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 126 | |
| 2 | 2020 | 62 | |
| 3 | Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review Hit paper breakdown → | 2024 | 58 |
| 4 | 2022 | 31 | |
| 5 | 2021 | 25 | |
| 6 | 2020 | 24 | |
| 7 | 2019 | 22 | |
| 8 | 2021 | 21 | |
| 9 | 2020 | 18 | |
| 10 | 2021 | 14 | |
| 11 | 2021 | 7 | |
| 12 | 2023 | 5 |
About Amina Bolatkan
Amina Bolatkan is a scholar working on Molecular Biology, Cancer Research, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Infectious Diseases, having authored 12 papers that have together received 413 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (3 papers), RNA modifications and cancer (3 papers), AI in cancer detection (3 papers), Cancer-related gene regulation (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), COVID-19 diagnosis using AI (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). The work is most often cited by research in Health Informatics (61 citations), Radiology, Nuclear Medicine and Imaging (147 citations), Cancer Research (78 citations), Artificial Intelligence (114 citations) and Health Information Management (15 citations). Amina Bolatkan has collaborated with scholars based in Japan, United Kingdom and United States. Frequent co-authors include Ryuji Hamamoto, Masaaki Komatsu, Ken Asada, Syuzo Kaneko, Kazuma Kobayashi, Ken Takasawa, Satoshi Takahashi, Norio Shinkai, Hidenori Machino and Akira Sakai. Their work appears in journals such as Biomolecules, Biomedicines, Cancers, Journal of Personalized Medicine and Journal of Medical Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.